Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries

Anonymous

Abstract:

Geocoding is the task of converting location mentions in text into structured geospatial data.
We propose a new prompt-based paradigm for geocoding, where the machine learning algorithm encodes only the location mention and its context. We design a transformer network for predicting the country, state, and feature class of a location mention, and a deterministic algorithm that leverages the country, state, and feature class predictions as constraints in a search for compatible entries in the ontology.
Our proposed architecture, GeoPLACE, achieves new state-of-the-art performance on multiple datasets.
Code and models are available at \url{https://<anonymized>}.

Paper Type: short

Research Area: Information Extraction

Languages Studied: English

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